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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Teknologi Informasi dan Ilmu Komputer ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Communications in Science and Technology TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali Buletin Ilmiah Sarjana Teknik Elektro IJITEE (International Journal of Information Technology and Electrical Engineering) Jurnal Abdimas Ilmiah Citra Bakti (JAICB) Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Mitra Akademia: Jurnal Pengabdian Masyarakat SPEKTRAL : Journal of Communications, Antennas and Propagation Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Shita Fitria Nurjihan
Politeknik Negeri Jakarta
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences

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YOLOv8-Based Detection of Convective Storm Clouds for Cumulonimbus Classification Rafsyam, Yenniwarti; Nurjihan, Shita Fitria; Rinaldi, Arief
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1854

Abstract

Cumulonimbus (CB) clouds are vertically developed convective systems that are capable of producing severe weather phenomena, including turbulence, heavy rainfall, and lightning. These phenomena pose a significant threat to aviation safety. This paper considers an automated CB cloud detection approach using the deep learning algorithm You Only Look Once version 8 on NOAA-19 satellite imagery. The images of 640 × 640 pixels each were labeled into two classes: CB and non-CB. In general, rotation, flip, and random brightening are performed to develop a more robust model. After 100 training epochs, the proposed model produced reliable detection performance, as evidenced by 1,694 TP (true positives), 438 FP (false positives), and 304 FN (false negatives) cases, with a precision of 0.79, recall of 0.84, and an F1-score of 0.81. Validation using METAR reports from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG) confirmed the consistency of the model with observed weather conditions. The results demonstrated that YOLOv8 could provide a rapid and reliable framework for real-time detection and classification of CB clouds, thereby enhancing situational awareness for aviation operations and facilitating the effectiveness of satellite-based early warning systems in convectively active tropical regions.
Co-Authors Abdul Aziz Achmad Munir Aini, Syifa Nurul Alica Dewanti Ananda, Fitri Elvira ANGELICA, MOLLIYANA TOTA Annisa Shafira Darmawan Ardina Askum Benny Nixon Benny Nixon DITA INDRA FEBRYANTI, DITA INDRA Erna Riani Fitri Elvira Ananda Harits Lukmanul Hakim Hasani, Rifqi Fuadi Irwan Prasetya Kurniawati, Nazmia Kusuma, Dandi Lestari, Annisa Fitri Molliyana Tota Angelica Mughni Tabroni Muhammad Arif Rahman Muhammad Faris Permana, Rafli Pranoto, Dwitio Ahmad Prasetya, Irwan Putri, Khomala PUTRI, KHOMALA ERNIA Rafif, Muhammad Rahman, Muhammad Arif Regitta Octavia Siswono Rifqi Fuadi Hasani Rifqi Fuadi Hasani Rinaldi, Arief Rizki Azka Fihi Aghnia Setiati, Anik Tjandra Shalsa Luthfita Chairiza Siahaan, Abelitho Sianipar, Virgie Aubrey Hemas Sirait, Ari Valentino Sofian, Rivaldi Adi Sri Danaryani Sutanto, Sutanto Toto Supriyanto Tri Handoko Yenniwarti Rafsyam